| Course title | Statistical analysis of multivariate data |
|---|---|
| Course code | KRP/INSAD |
| Organizational form of instruction | Lecture + Tutorial |
| Level of course | Master |
| Year of study | not specified |
| Semester | Summer |
| Number of ECTS credits | 4 |
| Language of instruction | Czech |
| Status of course | unspecified |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Lecturer(s) |
|---|
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| Course content |
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Nature of multivariate data. Exploratory data treatment. Statistical testing of multivariate data. Structure hidden in the data. Principal komponent analysis PCA. Factor analysis FA. Canonical correlation analysis CCA. Discriminant analysis DA. Logistic regression LR. Cluster analysis CLU. Multidimensional data analysis MDA. Correspondence analysis CA.
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| Learning activities and teaching methods |
| Monologic (reading, lecture, briefing), Methods of individual activities |
| Learning outcomes |
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The application of computer oriented statistical methods in scientific and technical fields enables not only the use of information hidden in data but also the creation of models, optimizations, and possible solutions. It is a multi-disciplinary movement on the frontier of the scientific disciplines of statistics and informatics. The goal of multivariate data processing is to classify data according to many variables and to find hidden structure and interrelationship among these variables. The objective is to find a way of condensing the information contained in a number of original variables into a smaller set of variables with a minimum loss of information. The objective is to classify a sample of entities into a small number of mutually exclusive groups based on the similarities among the entities.
Evaluation of experimental data independently. |
| Prerequisites |
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Basic knowledge of mathematics and statistics.
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| Assessment methods and criteria |
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Written examination, Home assignment evaluation
Fulfilled subject INSZD. |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester | |
|---|---|---|---|---|
| Faculty: Faculty of Electrical Engineering and Informatics | Study plan (Version): Process Control (2016) | Category: Special and interdisciplinary fields | - | Recommended year of study:-, Recommended semester: Summer |
| Faculty: Faculty of Electrical Engineering and Informatics | Study plan (Version): Process Control (2014) | Category: Special and interdisciplinary fields | - | Recommended year of study:-, Recommended semester: Summer |
| Faculty: Faculty of Electrical Engineering and Informatics | Study plan (Version): Information Technology (2016) | Category: Informatics courses | - | Recommended year of study:-, Recommended semester: Summer |
| Faculty: Faculty of Electrical Engineering and Informatics | Study plan (Version): Information Technology (2014) | Category: Informatics courses | - | Recommended year of study:-, Recommended semester: Summer |
| Faculty: Faculty of Electrical Engineering and Informatics | Study plan (Version): Information Technology (2015) | Category: Informatics courses | - | Recommended year of study:-, Recommended semester: Summer |
| Faculty: Faculty of Electrical Engineering and Informatics | Study plan (Version): Process Control (2013) | Category: Special and interdisciplinary fields | - | Recommended year of study:-, Recommended semester: Summer |
| Faculty: Faculty of Electrical Engineering and Informatics | Study plan (Version): Process Control (2015) | Category: Special and interdisciplinary fields | - | Recommended year of study:-, Recommended semester: Summer |